Unexpected NaN values in loss during TensorFlow training with custom loss function
I'm refactoring my project and I've been struggling with this for a few days now and could really use some help... I'm working with an scenario while training a neural network using TensorFlow 2.8.0, where the loss becomes NaN after a few epochs. I'm using a custom loss function for a regression task that seems to be the source of the question. Here’s the loss function I implemented: ```python import tensorflow as tf def custom_loss(y_true, y_pred): loss = tf.reduce_mean(tf.square(y_true - y_pred)) return loss ``` I've also checked my data, and it looks fine, with no NaN values or infinite values. However, during training, the loss starts at a reasonable value but eventually becomes NaN: ```python epochs = 100 model.compile(optimizer='adam', loss=custom_loss) model.fit(x_train, y_train, epochs=epochs, validation_data=(x_val, y_val)) ``` To debug this, I've added some print statements in the `custom_loss` function to see the values of `y_true` and `y_pred` before calculating the loss. It seems that `y_pred` is becoming extremely large after a few epochs, causing the squared difference to overflow. I also tried normalizing my output data by applying MinMax scaling, but the scenario continues. Can anyone suggest how to prevent the NaN values from appearing in the loss function or point out potential pitfalls in my implementation? Is this a common scenario with custom loss functions in TensorFlow, and what are some best practices to avoid it? This is part of a larger API I'm building. What's the best practice here? I'm using Python latest in this project. My development environment is Windows 10. I'd really appreciate any guidance on this.